AHU PAKDEMİRLİ, Asım LEBLEBİCİ

AI in Medicine versus AI in Prehospital

This study was made to compare the information about the studies on “Artificial intelligence in medicine” and “Artificial intelligence in Pre-hospital” on the Scopus database, and to investigate research on artificial intelligence in health and pre-hospital field. In the study, the studies on “Artificial Intelligence in Medicine/Prehospital,” “Machine learning in Medicine/Prehospital” and “Deep learning in Medicine/Prehospital” on Scopus database were performed to compare the information. Two groups were examined according to “Year”, “Author”, “Institution”, “Publication type”, “Field”, “Country”, “Fund institution”, “Language” and “Citation” parameters. Descriptive statistics and nonparametric Mann-Whitney-U test was used. In the studies conducted, the rate of change in the 2 screening groups was calculated around 83-84%. Although there is no statistical difference between the rate of change, it is seen that the concept of “Artificial intelligence in medicine” is used quite widely compared to “Artificial intelligence in Pre-hospital”. It is seen that, as in all areas of Artificial Intelligence and its sub-concepts, the studies carried out in the fields of health and pre-hospitals continue and will continue to increase dramatically.

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1. Amisha PM, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. J Family Med Prim Care 2019;8:2328–31.

2. CB Insights Research. Healthcare remains the hottest AI category for deals. https://www.cbinsights.com/research/artificial-intelligence-healthcarestartups- investors/ access date 20.12.2019.

3. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25:44-56.

4. Ahuja AS. The impact of artificial intelligence in medicine on the future role of the physician. Peer J. 2019;7: e7702.

5. Vinyard M, Whitt J. Scopus. Charleston Advisor. 2016;18:52-7.

6. Sinsky C, Colligan L, Li L,et al. Allocation of physician time in ambulatory practice: a time and motion study in 4 specialties. Ann Intern Med. 2016;165:753-60.

7. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542:115-8.

8. Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology. 2017;284:574-82.

9. Ribli D, Horváth A, Unger Z, Pollner P, Csabai I. Detecting and classifying lesions in mammograms with deep learning. Sci Rep. 2018; 8:1-7.

10. Le EP, Wang Y, Huang Y, Hickman S, Gilbert FJ. Artificial intelligence in breast imaging. Clin Radiol. 2019;74;357-66. doi: 10.5455/medscience.2020.09.9186 Med Science 2020;9(2):293-7 297